Over the last few years, variational quantum algorithms (VQAs) have emerged as a leading strategy to realize a quantum advantage on NISQ devices. Specifically, VQAs employ shallow circuit depths to optimize a cost function, expressed in terms of an ansatz with tunable parameters, through iterative evaluations of expectation values [1]}. Applications of VQAs include the variational quantum eigensolver (VQE) for finding the ground or excited states of a system Hamiltonian [2]}, [3]}, [4]}, the quantum approximate optimization algorithm (QAOA) for solving combinatorial optimization problems [5]}, and solvers for linear [6]}, [7]}, [8]} and non-linear [9]} systems of equations.
